Zobrazeno 1 - 10
of 838
pro vyhledávání: '"Suriawinata, A."'
In digital pathology, whole slide images (WSIs) are widely used for applications such as cancer diagnosis and prognosis prediction. Visual transformer models have recently emerged as a promising method for encoding large regions of WSIs while preserv
Externí odkaz:
http://arxiv.org/abs/2304.07434
Publikováno v:
Journal of Pathology Informatics, Vol 15, Iss , Pp 100386- (2024)
In digital pathology, whole-slide images (WSIs) are widely used for applications such as cancer diagnosis and prognosis prediction. Vision transformer (ViT) models have recently emerged as a promising method for encoding large regions of WSIs while p
Externí odkaz:
https://doaj.org/article/4022cdf7cff34716999664b1003c89a7
Publikováno v:
In Journal of Pathology Informatics December 2024 15
In pathology, whole-slide images (WSI) based survival prediction has attracted increasing interest. However, given the large size of WSIs and the lack of pathologist annotations, extracting the prognostic information from WSIs remains a challenging t
Externí odkaz:
http://arxiv.org/abs/2110.11558
Autor:
Wei, Jerry, Suriawinata, Arief, Ren, Bing, Liu, Xiaoying, Lisovsky, Mikhail, Vaickus, Louis, Brown, Charles, Baker, Michael, Tomita, Naofumi, Torresani, Lorenzo, Wei, Jason, Hassanpour, Saeed
With the rise of deep learning, there has been increased interest in using neural networks for histopathology image analysis, a field that investigates the properties of biopsy or resected specimens traditionally manually examined under a microscope
Externí odkaz:
http://arxiv.org/abs/2101.12355
Autor:
DiPalma, Joseph, Suriawinata, Arief A., Tafe, Laura J., Torresani, Lorenzo, Hassanpour, Saeed
Developing deep learning models to analyze histology images has been computationally challenging, as the massive size of the images causes excessive strain on all parts of the computing pipeline. This paper proposes a novel deep learning-based method
Externí odkaz:
http://arxiv.org/abs/2101.04170
Autor:
Zhu, Mengdan, Ren, Bing, Richards, Ryland, Suriawinata, Matthew, Tomita, Naofumi, Hassanpour, Saeed
Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC
Externí odkaz:
http://arxiv.org/abs/2010.16380
Autor:
Wei, Jerry, Suriawinata, Arief, Ren, Bing, Liu, Xiaoying, Lisovsky, Mikhail, Vaickus, Louis, Brown, Charles, Baker, Michael, Nasir-Moin, Mustafa, Tomita, Naofumi, Torresani, Lorenzo, Wei, Jason, Hassanpour, Saeed
Applying curriculum learning requires both a range of difficulty in data and a method for determining the difficulty of examples. In many tasks, however, satisfying these requirements can be a formidable challenge. In this paper, we contend that hist
Externí odkaz:
http://arxiv.org/abs/2009.13698
Autor:
Wei, Jerry, Suriawinata, Arief, Liu, Xiaoying, Ren, Bing, Nasir-Moin, Mustafa, Tomita, Naofumi, Wei, Jason, Hassanpour, Saeed
The unique nature of histopathology images opens the door to domain-specific formulations of image translation models. We propose a difficulty translation model that modifies colorectal histopathology images to be more challenging to classify. Our mo
Externí odkaz:
http://arxiv.org/abs/2004.12535
Autor:
Harrington, Lia X., Wei, Jason W., Suriawinata, Arief A., Mackenzie, Todd A., Hassanpour, Saeed
Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence. We used natural language processing to extract polyp morphological charac
Externí odkaz:
http://arxiv.org/abs/1911.07368